Research On Customer Segmentation Of Retail Industry

As the globalization and development of internet intensifies, modern enterprises face very violent and fierce surviving competition, and now customer resource has become the determinant factor, relying on which enterprises survive and thrive, customer-focus gradually gains the popularity and we're now living in a CRM era. Meanwhile, the OLTP with characteristics of real-time, multiple user and database environment becomes the daily practice as the information technological improvement goes further. The bar-coding technology empowers the OLTP capability of retail industry also with the generation of huge amount daily data. Nowadays the OALP leads the way which orientates rational decision-making support bases on the intelligent data analyzing, therefore the intelligent technologies such as Data Mining and Data Warehouse follow the rational line to become mainstream.The exponential data accumulation and fast development of intelligent technology bring along both opportunity and challenge. Confined to the limited resources, the enterprises have to focus on the most valuable customers which is consistent with the classic Pareto's Principle-The 80/20 Rule: 80% whole profit is contributed by 20% of the whole customers. Thus it carries significant implication that enterprises dig out valuable customer and treat them with differentiated and personalized service and product which are key factors in enhancing enterprise's competitive advantage. Data Warehouse (DW) integrates data from many sources to provide an information pool for business queries. DW provides not only the spectacular data collection and management platform, what's more important is the very powerful analyzing tools which plays a more and more significant role in improving the customer service and enhancing customer satisfactory degree and loyalty, and ultimately advances the enterprises' competitive advantage.The paper strives to apply the data warehouse and data mining technology to segment the customer in retail industry, and based on the different customer cluster, provides differentiated analysis and service. The paper is constructed as follows: firstly, grounded on the summarized research of domestic and abroad customer segmentation theories, the segmentation factors for in this research are presented; secondly, assisted by the powerful Microsoft SQL Server 2000 and Analysis Server (MSAS for short) environment, the customer information data warehouse is created; thirdly, the customer segmentation based on the data source of FoodMart super market is realized by data mining standard process: CRISP-DM; the application research based on segmented customer such as the customer profiling and churn analysis is finally provided along with the sum-up and prospect anticipation.